Online shopping has become a common means of shopping, and a large selection of merchandise exists on websites such as Taobao and Tmall for consumers to choose from. However, due to the large selection of merchandise, consumers typically have to spend a great deal of effort to locate an appropriate product. When consumers indicate an interest in a product (by clicking or bookmarking the product), similar products in the Web can be automatically located to help reduce the effort spent by the consumer in locating appropriate products. Consumers would thus avoid numerous searches and price comparisons, and reduce overall efforts in purchasing a product. In particular, when an original product selected by a consumer is inappropriately priced, missing a size, or unsatisfactory in some other aspect, the consumer would like to conveniently continue to browse through the merchandise. Thus, bounces are avoided, and conversion rates are increased. A conversion rate refers to the probability a customer would click a product on the page to obtain a more detailed web page during a browsing session. A bounce typically means that a customer gives up on locating the appropriate product and leaves the web page. The bounce also indicates that the present web page is not satisfactory.
On shopping websites, shopping advice columns or special articles are frequently written to guide purchases. These columns or articles present products, and the presented products are consistent in terms of design and style and comply with seasonal marketing themes. Prior to similar product recommendation engines, selections were typically made manually from a large pool of products. This selection process expended a large amount of manual effort and did not ensure a definite recall rate. With similar recommendation technology, the selection process only needs to designate seed products. The seed products refer to sample products, from which the recommendation engine could analyze visual elements, to look for more similar products. The similar product recommendation engine then quickly and accurately locates similar products throughout the Web, and automatically creates special articles or columns.
Currently, many implementation schemes for similar product recommendation technologies exist. A traditional similar product recommendation technology is based on text recommendations. In other words, product inter-relationships are established through descriptive text relating to the products themselves. Typically, the descriptive text is written by website sellers, and quality of the descriptive text varies greatly. Often, fraudulent conduct involving various kinds of inappropriate titles or descriptions occurs. Accordingly, recommendation results of the traditional similar product recommendation technology may have limited utility.
Another popular recommendation technology involves the following: products are recommended to consumers by relating user behaviors, such as repeated viewing or repeated purchases, to the products. In other words, the recommendation technology involves the following: by reviewing historical data, the system finds that most consumers of one type have expressed an inclination towards this product. The recommended products are those products towards which the type of consumers has also been typically inclined. This recommendation technology can increase conversion rates. However, since the recommendation technology does not include having an understanding of the product content, but instead merely simulates consumer habitual behavior, the recommendation results of the recommendation technology cannot be controlled. Thus, ensuring consistency or stability of the recommendation results of the recommendation technology is difficult. For example, the system is not capable of knowing if the reason for the relationship between a product in which a consumer is interested and recommended products is due to color matching, style design, artificially guided traffic behavior, or merely the fact that two products are placed close to each other. Therefore, this type of recommendation technology can only recommend products to consumers, and cannot serve as a product recommendation engine. In particular, this type of recommendation technology relies on vast amounts of historical data, and can run into cold starts, data sparsity, and other such problems which can affect the recommendation results. Data sparsity refers to a difficulty in recommending similar products due to a lack of related historical data. A cold start is a specific example of data sparsity, which refers to a recommendation engine just beginning to execute so most products to be recommended suffer from a lack of historical data.